The Quantitative Peace

While the IR people are away, the Americanists will play.

Editor's note: This is a guest post by Joshua N. Zingher, a doctoral candidate in the Department of Political Science at Binghamton University. The content of this post is based on Josh's paper, ‘An Analysis of the Changing Social Bases of America's Political Parties: 1952-2008’, appearing in Electoral Studies. This piece is cross-posted at the London School of Economics' USAPP Blog, which can be found here. 

 

Demographic changes mean that traditional Republican constituencies are shrinking as the Democrats’ grow.

It is difficult to discuss electoral politics in the United States without talking in terms of social groups.  Journalistic accounts of party competition often stress the important role that specific constituencies play (such as the “evangelical” or “Latino” vote) in determining the outcome of presidential elections.  A great deal of political science research has shown that these social cleavages structure party competition.  However, the political importance of these social cleavages can change, which can lead to a reshuffling of the social bases of the party coalitions.  In addition, demographic changes also have the potential to dramatically reshape the political landscape, as new groups enter the electorate and the ratio of existing groups’ changes.  I am interested in how changes in the cleavage structure and changes in the ratio of groups work in conjunction to transform electoral coalitions.  In new research, I investigated two related questions: what group memberships influence individual level voting behavior and how has the number of votes each political relevant group contributes to the party coalitions changed over time?

 

Identifying the Group Bases of Party Support 

So what groups are politically relevant?  To answer this question, I conduct a multivariate statistical analysis utilizing nearly 60 years of American National Election Survey (ANES) spanning 1952 to 2008.  I utilized group memberships as independent variables and vote choice as the dependent variable.  The battery of group memberships included variables for class, race, ethnicity, region, education, birth cohort and age. I define politically relevant group memberships as those that exert a statistically significant influence on individual level vote choice.  I ran a separate regression for each decade, which allowed me to assess whether the effect of specific group memberships have changed over time.  The substantive results of these models are displayed in Figure 1 below.

 

Figure 1 – Predicted effect of group membership on vote choice in presidential elections

 

 

The results of the analysis displayed in Figure 1 demonstrate that the following group memberships lead individuals to be consistently and statistically significantly more likely to support one party over the other: 

 

Table 1: The Core Democratic and Republican Social Groups 

Democratic Groups	Republican Groups
African Americans	Whites
Latinos	Protestants
Catholics	Weekly Church Attendees
Union Members	Southern Whites
Non-Religious	Men
College Graduates
Women Jews

 

This analysis highlights the continuity and change that characterizes the parties’ social bases.  Some groups have been stable members of a party coalition for the last 60 years.  African Americans, Catholics, union members and Jews have all been consistent members of the Democratic coalition while whites and Protestants have reliably favored the Republicans. 

Yet, other groups’ political leanings have changed considerably.  Whites in the Southern United States were once reliably Democratic voters; today they are one of the most solidly Republican constituencies.  The divide between the religiously devout and the secular has also become more politically important in recent elections.  Non-religious voters have become increasingly likely to vote for Democratic candidates while weekly church attendees have become more likely to support Republicans.  Similarly, a partisan “gender gap” emerged in the 1980s.  Men have become significantly more likely to vote for Republican candidates while women have become more likely to support Democrats.  In sum, many traditionally politically important social cleavages have endured (e.g. black/white) while other cleavages have become politically important only in recent decades (secular/religious or female/male).

           

Group Contributions and the Importance of Demographic Changes

The preceding analysis established what group memberships significantly influence vote choice, however, simply understanding what group memberships shape individual level voting behavior paints an incomplete picture of electoral change.  Winning an election is not about having core groups of loyal supporters.  Rather, parties must be able to obtain a majority of votes in order to win elections.  Thus, any process that leads to a party gaining more or less from a specific social group is potentially politically consequential.  Figures 2 and 3 show my calculations for each group’s contribution to the Democrats’ and Republicans’ party coalitions, as measured as a proportion of the party’s total votes. There have been considerable changes in the size of group contributions to the party coalitions—many of which highlight the important role that demographic shifts play in reshaping the party coalitions. 

 

Figure 2 - Group contribution as a proportion of Democrats’ total votes: 1952-2008

 

 

Figure 3 - Group contribution as a Proportion of Republicans’ total votes: 1952-2008

 

The Democratic Party has been obtaining an increasing proportion of its support from groups that are growing as proportion of the electorate, such as Latinos, college graduates and the non-religious. In addition, increasing turnout among Latinos and African Americans has also led to the size of these groups’ contributions to the Democratic coalition.  The Republican Party is being forced to confront the opposite scenario—many Republican groups, whites, Protestants and weekly church attendees, are making up a continuously shrinking proportion of the electorate.  Thus far, the Republican Party has been able to offset demographic changes via high turnout among their core groups as well as through winning an increasingly large majority of the Southern white vote.

So what are the conclusions can be drawn from this analysis? Firstly, the group sources of party support are dynamic, the political relevance of certain social cleavages ebb and flow across decades, which is evidenced by the changing relationship between group membership and vote choice.  Secondly, demographic changes can be politically consequential.  The Republican Party is currently in the undesirable position of having to find new sources of electoral support in order to remain viable in future elections.  The Republican Party’s need to find new sources of electoral support has come about not as a result of the loss of electoral support among loyal groups—rather, the Republican Party must find new sources of electoral support simply because traditionally Republican constituencies are making up a shrinking proportion of the electorate.     

 

 

 

The Dark Knight Rises and the Economic Impact of Bane's Occupation of Gotham

I started working on this post a long time ago and, for whatever reason, never got around to finishing it. So please keep in mind that this was largely written shortly after the film first came out. I should also disclose at the outset that this post will contain spoilers, so if there is some sort of unbelievably powerful force that has kept you from seeing this fantastic movie, please be warned. For those still interested, there's more after the jump.

Educational Attainment and Race in Alabama

A part of my responsibilities as a post-doc here at Alabama entails helping faculty with seeking out external funding for projects. One of these projects has involved developing a program to promote educational outreach throughout the state. In the course of this work I've been digging through some data on educational attainment rates in Alabama. In terms of learning about the political and economic characteristics of my new state, this has been an informative exercise. I also think it's helpful in illustrating the benefits of digging through your data, and helps to shed some light on the benefits of considering the distribution of your data more carefully, and so I thought I'd write a quick post on the subject. For the purposes of this post I'll be drawing on data collected from the American Community Survey's 2006–2010 five year population estimates. 

The mean percentage of Alabama's population holding a bachelor's degree or higher for this time period is 21.7%.  Compare this figure with that of my home state—New York's overall rate is 32.1%. This is a fairly large disparity, but further disparities emerge when we begin to disaggregate the data by race. The graph below shows the mean value of this same variable across the black and white populations of both states. 

From this we can see that the total values mask some important differences between groups within each state. In both cases the college attainment rate of the white population is slightly higher than the overall mean value. Alternatively, the attainment for the black population is significantly lower than the overall mean value in each state. Even New York, which on its face appears to have a significantly higher overall college attainment rate, is not much different from Alabama when we compare black college attainment rates with Alabama's values. 

Even when disaggregating the data in this way, we often still lean on the mean value pretty heavily. The distribution of the data around the mean can be incredibly informative as well, but we don't often pay as much attention to this as we should. Below I present a quick illustrative example of why this can matter.

Looking at Alabama's data again, I have drawn on county-level college attainment data for both groups.

Looking back at the first graph we can see that the statewide mean white college attainment rate was about 24%, and the statewide black attainment rate is roughly 14%. Looking at the county-level distributions can help us to get a better grasp on educational attainment rates throughout the state. In the case of white attainment, we can see that the distribution is slightly skewed towards the low end, but the lowest value in the distribution is around 8–9%. Alternatively, the distribution of black college attainment rates at the county level is more heavily skewed, and the lowest value in the distribution is considerably lower than for the white attainment rates, as shown in the preceding graphs where the X-axes are held constant to help make the figures more comparable. In fact, .30% is the lowest black attainment rate recorded in the data. 

Looking at the distribution of the data in addition to the mean values for these different groups can provide some additional insights into the substantive issues we're interested in. In this case, it's already apparent from considering the mean values that there are disparities in the college attainment rates of the black and white segments of the state's population. However, these values may actually underestimate the magnitude of those disparities. Though the distribution for white attainment rates is still skewed, it's floor is considerably higher than that of black attainment rates. In fact, by my count there were 14 counties in which the recorded black attainment rate was lower than the lowest recorded white attainment rate. The maximum for each group is roughly the same, however. Areas around Tuscaloosa, Birmingham, and Huntsville have fairly high college attainment rates. But these areas are highly unrepresentative when considering the state more broadly. In fact, although the mean statewide black attainment rate is 14%, the majority of counties fall well below this figure. And while most counties also fall below the mean on white attainment rates, there are, comparatively, more counties falling above the mean. All of this suggests that the mean statewide black college attainment figures are more heavily influenced by outliers in the data than are white attainment rates.  

Lastly, looking at these plots reveals another important feature of the distributions—the variance. The variance in white college attainment rates is 66.2. The variance in black college attainment rates is nearly half that, at only 37.5. This tells us that not only are black college attainment rates lower than white attainment rates, but they are more uniformly so. To put it differently, there is greater dispersion in white college attainment rates, while black college attainment rates are more tightly clustered around lower values. This is made apparent by looking at the two preceding graphs. Though both are skewed towards the lower end of the X-axis, white college attainment figures are less tightly clustered here, as I mentioned above.

This is an issue that I'll come back to in the future, but for now it seemed like this was a useful example for illustrating the importance of familiarizing yourself with the data that you're working with, and how relying less on simple mean values can reveal interesting features of the data you're working with. Bear Braumoeller has an interesting Political Analysis piece about examining the variance, which is worth checking out. More substantively, I also think that this exercise helps to more fully draw out the disparities that may exist between social groups. Relying on simple indicators of central tendency can be useful in some contexts when we want to quickly convey bits of information, but these figures can also be misleading. At the very least, they don't always tell us the full story.

 

Teaching Introductory Graduate Methods: Some Ideas About Understanding Sampling Distributions

Everyone has their own way of teaching methods, but in this entry I thought I’d share a brief exercise that I thought worked well. This code was developed when I was the teaching assistant for a second-semester methods class focusing on regression. Specifically, this was a section of the class from the first few weeks that dealt with two things: first, showing/repeating the idea of a sampling distribution, and second, showing some introductory code on loops and graphics.

The code uses the ggplot2 package for R. This software, and this package, have (in my experience) a somewhat steep learning curve so starting slow is important, even in the second semester. Equally, the concept of a sampling distribution is something that takes a while to click with most people who don’t have a statistical background. So what I’m trying to do with this code is show some basic graphics, and also introduce sampling distributions by ‘bringing one to you on a plate’.

            The first part of the code simulates ten rolls of a (pseudo) six-sided die:

cenlt<-vector(length=10)

for (i in 1:10)
{

            x<-runif(10,1,6)

            cenlt[i]<-mean(x)

}

cenlt

Here we create a vector, called cenlt (named for the central limit theorem), of length ten. We don’t give R any instructions about what to put in this vector, so if we inspect it (typing “cenlt”) it will display ‘FALSE’ ten times. We leave this empty vector to one side. Then we create a new imaginary conveyor belt, telling R to sit beside the conveyor belt and perform the same operation every time the belt rolls past, which it will do ten individual times. 

            The operation R will perform is to create ‘x’, which is a set of ten numbers, each randomly sampled from a uniform distribution between 1 and 6. We then take the mean of this set, and store it as a new element of cenlt. When we’ve done this ten times, then, cenlt will be full. We then display cenlt. We can actually display it a little bit more attractively if we graph it: 

library(ggplot2)

p1<-ggplot()

p1<-p1+geom_histogram(aes(x=cenlt),binwidth=0.2,
linetype=1, alpha=.3, color="black", fill="red")

p1<-p1+ ylab("Count") +
xlab("Sample Mean") + xlim(c(0,6)) + ylim(c(0,6))

p1<-p1+opts(title="Distribution of Sample
Means")

p1<-p1+geom_segment(aes(x = 3.5, y =0, xend =
3.5, yend =6))

p1

            The first line creates a new object called p1, which we set to be a graph object. Then we take it, and add another layer. The second line tells it to fill the graph with the shapes necessary to make a histogram: aesthetically, this means an x-axis defined by the values of cenlt. We ask for the shapes to have a solid black outline (linetype = 1, color = “black”) and to be semi-transparent and red (alpha = 0.3, fill = “red”). We then add a line showing the expected mean (geom_segment) as well as other details. 

            This code was then followed by a discussion of what each element of ‘cenlt’ represented. Each was a mean. So the mean of cenlt was a ‘mean of means’, and so cenlt has its own distribution: the distribution of means of ‘x’, and if we consider each x to be a sample, then seeing cenlt as the sampling distribution is relatively straightforward. The code can then be tweaked to make ‘x’ bigger, or to run ‘x’ more times, and discussing each of these options helps elucidate the idea of a sampling distribution. Changing the distribution away from uniform can also be useful. We also got some good discussions out of manipulating the basic parameters of the graph (the axis labels, the colors and binwidth of the histogram), and then later by switching the geoms to different options. All in all, this was a useful exercise that led to some good discussions, and helped introduce the ideas of sampling distributions, loops, and statistical graphics.

 

Identifying Cities with the Safest Drivers

In May 2013 Allstate released its "America's Best Drivers Report", which identifies cities with residents who have the longest years between accidents. Boise is #2 on the list, so it caught my attention. The data are based on Allstate insurance claims from January 2010 to December 2011.  

Although the report is interesting, I think the name of the report is misleading (which is clear when you read how Allstate discusses the report). The report may not necessarily identify where the safest drivers are. Instead it may identify cities where environmental conditions make it safer to drive. If that is the case, the drivers are not necessarily better in these cities, they are just driving in better conditions, leading to more years between accidents. Also, it is possible that the report be identifying cities where accidents are more likely go unreported, which would make it seem like drivers in those cities have more years between accidents. 

An alternative way to determine where the safest drivers are is to predict the average time between accidents in these cities based on environmental conditions and likelihood of reporting an accident, and then compare those predictions with the actual averages. If a city did worse than predicted, then perhaps those drivers are less safe. If a city did better than average, those cities may be more safe.

I collected data on factors that influence accident reporting (which include whether an accident will occur, and if so, if it will be reported). Then I made a series of predictions for each city and compared those predictions with the actual data. I identified four cities that do worse than predicted and four cities that performed better than predicted. All the other cities performed as expected.

Likelihood of Reporting Variables

I have identified two potential reasons why motorists might not report an accident. First, not all states require motors to automatically report accidents to the police. In most states motorists only have to report an accident if there is a certain amount of property damage (or if someone dies as a result of the accident). For the minimum reporting variable, I collected data on the minimum amount of property damage needed in to report an accident for the state a city was located in. For states with no minimum, I set the value to $5000.

I expected the sign of the minimum reporting variable to be positive. In places with higher minimum property damages requirements, minor accidents are less likely to be reported, increasing the measured years between accidents. Hence the higher the amount of minimum property damage to report in a city, the higher the average years between accidents in those cities. 

Income may also have a positive or a negative effect on whether someone reports an accident, which would decrease or increase the average time between accidents.

Individuals with higher income are more likely to have insurance, and hence more likely to report an insurance claim when in an accident. This would reduce the measured years between accidents. For the income variable, I took the log of median household income for the state each city was located in 2005. The sign of this variable could be negative: cities with higher average income have less years between accidents.

On the other hand, someone with higher income may be less likely to report an accident because they can pay for the damages. This would increase the measured years between accidents. So I expected that the sign of this variable could also be positive: cities with higher average income have more years between accidents.

Environmental Conditions

Besides reporting requirements, several environmental factors that may lead to more collisions. More densely populated cities may create more opportunities for drivers to get into accidents. I collected data on population density of these cities and logged the values. I expected the sign of this variable to be negative: individuals in more densely populated cities have less years between accidents.

Weather conditions could also lead to more accidents. For this measure, I took the average rainfall in the cities in the data set. I expected the sign of this variable to be negative: as rainfall increases in a city, the time between accidents should decrease.

More bike friendly cities may have less accidents on average because there there are less drivers on the road. This would increase the time between accidents. For this variable I used data from Bicylcing magazine's Top 50 bike friendly cities in the US. I expected the sign of this variable to be positive: individuals who live in more bike friendly cities should have more time between accidents.

Teenagers may be more likely to get into accidents than adults. So as the minimum driving age decreases, the time between accidents should decrease. I collected data on the minimum driving age in each state a city was located in. I expected the sign of this variable to be positive: in cities with higher minimum driving age, time between accidents should increase.

Higher speed limits may also lead to less measured time between accidents. Although most studies suggest that higher speed limits do not increase the number of collisions, they may increase the number of severe collisions. If higher speed limits may lead to more severe collisions, this will reduce te time between accidents because more severe collisions are more likely to be reported. For this variable I collected data speed limits in the states the cities are in. I expected the sign of this variable to be negative: higher speed limits lead to less time between accidents.

The Results

The results in the table below suggest that individuals in cities with higher median income have less time between accidents. This may provide support for the argument that wealthier people are more likely to have insurance, so they are more likely to report a claim. The table also demonstrates that as population density and rainfall increases in a city, time between accidents in that city decreases. This suggests that environmental factors play a role in frequency of accidents. Higher speed limits also decrease the time between reported accidents. 

Now to the predictions. The table below presents the predictions for each state and compares them with the actual values. Four cities did better than average: Fort Collins, CO, Brownsville, TX, Madison, WI, and Visalia, CA. And four cities did worse than average: Anchorage, AK, Portland, OR, San Francisco, CA, and Glendale, CA. But for the majority of cities, they performed as expected giving the living conditions in those cities. 

So the take away message: drivers in most cities perform as expected. There are several cities where drivers have long years between accidents, but they drive in better conditions. And there are several cities where drivers have very few years between accidents, but they drive in more hazardous conditions. Drivers should be judged on the probability that they would get into an accident given the conditions they live in. That way it is easier to determine whether they are being safer than expected or more reckless than expected.

 

Political Science and Scientists in the News

Just a quick link to this Atlantic article. It features several works by political scientists on why and when the public is likely to support military action. Complete with graphs and everything. 

 

*Picture taken from the aforementioned and linked Atlantic article. 

Follow-up

Just a quick follow-up to yesterday's post. Chad sent me a link to this paper that was recently posted on AJPS' early view. I've not read the entire article yet, but it seems apropos given the subject matter of yesterday's piece. 

Of QP Publications and International Relations

Two new pieces from QP authors.  First, Michael Flynn has a solo-authored piece in an upcoming Foreign Policy Analysis that has an early view avaible. Abstract: 

How does political competition among domestic actors influence foreign policy choice? Studies examining these questions often focus on the role of economic or partisan interests, and how they influence the preferences of decision makers who are subject to electoral institutions and pressures of their constituents. Less attention has been paid to how the preferences of other influential but unelected actors influence state behavior. I examine the influence of one such group by looking at how American military leaders shape decisions on military spending and force structure, while also examining how these decisions have been affected by changes to the institutions governing civil–military relations. Results indicate that military leaders occupying key positions can influence defense spending priorities in favor of their respective branches. Results also suggest the influence of military leaders has changed and is conditional upon the institutions governing the relationships between civilian decision makers and military leaders.

Chad Clay, Michael Flynn, and Colin Barry have also landed an International Studies Quarterly article (currently available in early view) which argues the following: 

Nonstate actors, such as international non-governmental organizations (INGOs) and multinational corporations (MNCs), have attained an increasingly prominent role in modern world affairs. While previous research has focused on these actors’ respective interactions with states, little attention has been paid to their interactions with each other. In this paper, we examine the extent to which the decisions of private actors seeking to invest abroad are affected by the reputational costs of doing business in countries publicly targeted by human rights activists. We find that ‘‘naming and shaming’’ by human rights INGOs tends to reduce the amount of foreign direct investment received by developing states, providing evidence that INGO activities affect the behavior of MNCs. An additional implication of our findings is that shaming by INGOs can impose real costs on targeted states in the form of lost investment.

Chuck Hagel and Bipartisanship in Foreign Policy

The news surrounding Obama's nomination of Chuck Hagel and John Brennan as Secretary of Defense and Director of the CIA (respectively) has primarily been focused on the "controversial" Hagel's previous statements and positions. Republican commentators have been complaining about Hagel's nomination, claiming that his nomination sends a powerful signal that the Obama administration is putting more "daylight" between the US and Israel, among other things. Steve Clemmons at The Atlantic has also reviewed several arguments for and against Hagel's nomination. The turf on that subject has already been covered pretty thoroughly, so I'll leave the issue be for now. What has received less attention, however, is that Hagel's nomination represents a continuation of a fairly long history of Democrats appointing Republicans to positions within the foreign policy bureaucracy. 

In 1940 FDR appointed Henry Stimson and Frank Knox—both Republicans—to be his Secretary of War and Secretary of the Navy (again, respectively). Stimson's appointment in particular is generally regarded as marking the onset of the bipartisan consensus that characterized the early Cold War period. Having previously served as Secretary of State under President Hoover and Secretary of War under Taft, Stimson was already a widely recognized figure in the foreign policymaking community (such as it was back then). Along with Stimson came John McCloy and Robert Lovett, both of whom were also Republicans and both of whom served as Stimson's assistants in the War Department.

By bringing these Republicans into his administration, Roosevelt was attempting to build support among opposition Republicans for increasing US activism in World War II. Primarily this encompassed moderate internationalist Republicans from the Northeast. Even prior to his nomination, Stimson and his mentor, Elihu Root, himself previously a Secretary of War and Secretary of State, had petitioned Congressional leadership to introduce some form of draft bill to prepare the US for involvement in the growing conflicts in Europe and East Asia. Stimson and Root had both been devout internationalists in the years leading up to the Second World War, and their appointment marked what many historians have labeled as the beginning of the "Eastern Establishment" in foreign policymaking. The Establishment narrative, broadly speaking, tends to emphasize bipartisanship in the foreign policy bureaucracy specifically, but it also encompasses themes of elite influence, economic interests, and political polarization in foreign policymaking.

This general narrative has been present for several decades in the US foreign policy literature, but it's largely been isolated to the qualitative political science and historical literatures. Quantitative political science research has delved into the more narrow issue of bipartisanship, but these studies have tended to focus on Congress. Accordingly, the central focus of this large body of work has gone unexamined in a systematic sense, and part of my dissertation research has attempted to address this gap. This is really all a long-winded way of saying that the Hagel nomination provides me with a timely opportunity to briefly share some of my work on the subject. Though I don't want to give away the farm here, this seems like a fine time to chime in. 

Despite the recognition that the foreign policy bureaucracy—indeed, the bureaucracy more broadly—plays a fairly prominent role in the policymaking process, we don't really have much in the way of systematic quantitative research on the subject. I've tried to get at some of these issues in my dissertation by collecting some new data on appointees to foreign policymaking positions within the foreign policy bureaucracy. I primarily focus on the State Department and the Defense Department, but I also focus on some other agencies like the CIA. In total, I've collected data on some 1,000+ individuals serving in these posts from 1948 through the present. One of the primary characteristics that I coded for each individual was what, if any, party affiliation that individual had. As a way to get at trends in bipartisanship within the foreign policy bureaucracy, the graph below shows the percentage of appointments included in the data set that are bipartisan in nature.* 

Some interesting points: Perhaps not surprisingly, the Truman administration represents an all-time high in the data. This is partially a function of the fact that Truman inherited many of FDR's appointments, but it's also partially due to the fact that the foreign policy bureaucracy was undergoing some pretty radical changes during this time period. In particular, the size of these agencies grew dramatically during the World War II period. Coding rules are also partially responsible, as I focused on individuals occupying positions at the assistant secretary level and up (where appropriate). By the early to mid-1950s, however, the rate of growth slows a bit and, while still increasing in size, it does so more slowly than in the first few years of the Cold War. The crash at the beginning of the Eisenhower administration might reflect some of the antics of Joseph McCarthy and Secretary Dulles, both of whom were more than a little keen to go after anyone remotely suspected of being communists. It could also be a function of the Republicans having been out of power for about two decades. Also of note, the late 1970s do see a slight slump in the level of bipartisanship, which is potentially in line with arguments concerning the impact of Vietnam on bipartisanship more generally. The Clinton administration sees a fairly sharp spike near the end, and the Bush administration also sees a dip in its later years, while the incoming Obama administration sees a slight increase.

More broadly, the work that I've done so far suggests a few general trends. First, I find some evidence that, all else equal, Democrats tend to be more likely than Republicans to make bipartisan appointments to the foreign policy bureaucracy—Even controlling for the Truman administration. Other models suggest this difference is more acute in the early Cold War period (again, also while controlling for Truman). Additionally, I also find some evidence to support the conventional wisdom concerning the use of bipartisan appointments as a means of building moderate coalitions and as a response to domestic political conditions. In particular Congressional support matters, but is perhaps a little more nuanced than the historical accounts often suggest, as the availability of moderates in Congress also seems to condition this relationship. It also appears as though Presidents make these appointments with an eye towards governing more broadly, as the composition of the Senate alone appears to have little impact.

So what? Well, with respect to current news my work would suggest that Obama's retention of Bob Gates and his appointment of Chuck Hagel are actually very much in line with broader trends in Democratic foreign policy appointments since World War II. Robert Lovett—formerly Stimson's assistant at the War Department—went on to serve as Truman's Secretary of Defense. Kennedy subsequently offered Lovett his choice of State, Defense, and Treasury. Though Lovett turned him down, Kennedy did end up appointing Robert McNamara and McGeorge Bundy—both Republicans—to important posts. While there are certainly other lesser-known bipartisan appointees, these folks are some of the more prominent. Furthermore, these trends emerged well before the Republicans had fully taken on the identity of the party of national security. Still, even in the early Cold War period the Republicans did attack Democrats for being soft on Communism, so this vulnerability was present in some incarnation. Accordingly, Democratic administrations perhaps had more to gain from trying to draw in Republicans than vice versa, as such appointments could confer some measure of credibility. 

Moving forward it will be interesting to see how these trends continue to develop. Despite the much publicized opposition to Hagel's appointment it is entirely possible that this is just media hype and Republican chest-thumping. David Karol at the Monkey Cage recently wrote about the House Republicans' position-taking when voting on the recent Fiscal Cliff deal. So it's easy to imagine that the Republican outcry over Hagel's nomination is really going to wither when it comes time for the Senate to vote. Though these situations are not completely comparable, giving Obama a hard time seems to be a means of gaining currency in the Republican Party these days. Beyond the current confirmation battle, much of this will depend on how the current debate over the GOP's foreign policy positions pans out (see Drezner's latest Foreign Affairs piece, for example). If the Democrats manage to become the party of national security moving forward, perhaps we might see a reversal of these trends. Given the current Republican field, however, I wouldn't hold my breath.**

 * Given that none of this work/data is published (yet?) please don't cite this post or data. I'm still very much hoping to update and improve this material as I go. I guess this is kind of the norm, right?

** In the off chance that anyone comments, please be aware that I have had trouble leaving comments in the recent past. On the rare occasion that we do get feedback, I feel rude not replying, so apologies in advance if this should happen.

Mapping Leadership Tenure

Confession time.  One of my favorite subreddits on reddit is r/mapporn; a subreddit dedicated to the most visually appealing maps from all historical periods and dealing with a variety of topics.  We, at QP, cannot get our fill of maps as we (especially Michael Flynn) have blogged about it a few times (see here, here, and here). Yesterday, this map of leadership tenure was posted from The Economist:

 

 

The cutoff date of 1945 seems like a controversial choice and the single image of the map did not come with clear coding rules as how to decisions were made.  Being familiar with the Archigos data project put together by Kristian Skrede Gleditsch and Giacomo Chiozza (paper here), I figured this was an easy task to replicate to see what a common political science data set says about leadership tenure (one that I have used in my own research).  To replicate this, I created 1/1/1945 as the cutoff point and any existing regime is assumed to start freshly on that date (an untenable assumption of course).  This left me with the number of days of leadership that I divided by 365.24 (Leap years could be off, so I averaged the year length).  Then, by averaging out the leadership tenure for this time period until 2004, created a range of values for each country.  In Stata, creating a choropleth map is not overly difficult using spmap package and two different guides found here and here. 

 

Post-1945 Replication:

 

 

The pastel color provides a bit more contrast and makes the missing data cases easily identifiable. Some of this may be due to a quick use of the kountry package an dmissing a few obvious lacking cases.  Additionally, the data in Archigos goes back to 1875, so replicating the map again using the full data set is possible and done here:

 

 

Expanding the data backwards introduces a bit more variation than just starting from 1945.  Naturally, with two wold wars included, leadership turnover may be expected to be shorter in some countries.  Of note, the US average jumps up a category. 

 

Update: By request, a few maps that are more colorblind friendly. Blue: 

 

Greyscale: